1. Technical Field
Embodiments of the present invention relate to enhancing the quality of a stereoscopic image, more particularly to an apparatus, a method, and a recorded medium that can enhance the quality of a stereoscopic image by accurately correcting any color imbalance between the left image and right image forming the stereoscopic image.
2. Description of the Related Art
A stereo image refers to a left and a right image obtained by stereoscopic cameras. Depending on the radiometric properties of each stereoscopic camera and on changes in lighting conditions, there can be differences in the left and right images.
Differences between the left and right images may cause fatigue for persons viewing the stereo image, and as such, an algorithm for compensating such differences can be applied, including image color correction for compensating the luminance and chrominance between the left and right images.
Research on color correction for stereo images can be divided mainly into two types of methods.
The first method is to resolve color mismatching by using color response functions measured beforehand for the respective cameras. Here, a color response function can be obtained by independently correcting each camera by comparing the object being subject to correction with known colors. However, this method entails the inconvenience of having to repeat the complicated set of correction procedures every time there is a change in lighting conditions.
The second method is based on image processing and does not require a reference object for the correction. This method can be divided again into a global method and a local method.
The global method involves correcting color imbalance by applying a linear transformation to colors of the whole image. As this method does not consider partial lighting or the properties of individual colors, there is the drawback that the transformed image may partially include saturated or lost colors.
The local method, in order to supplement the drawback of the global method, applies different transformations for partial areas when correcting color imbalance, instead of applying a transformation on to the whole image. Unlike the global method, this method may require color matching, for which a feature-point matching algorithm is typically used.
If an insufficient number of color matching points are extracted for a partial area being transformed, the local method may provide unstable performance.
Also, the global method and the local method both performs the transformation separately for each channel, but since the channels of an image are interrelated, it may be difficult to accurately perform the transformation to a reference color when using independent processing for each channel.
Furthermore, even though the degree and tendency of color imbalance for a multi-view image may vary according to the color of the picture element, i.e., the position of the picture element color within a color space, existing methods apply the correction with a single transformation for picture elements of different colors, and thus cannot correct color imbalance accurately.